1. Technical Field
The present invention relates to a method and apparatus for detecting a pupil in an image of an eye, in particular a human eye. Such a method and system can be applied, for example, in a system for eye movement tracking.
2. Background Art
Information regarding the position and/or movement of the pupil can be used for tracking of the position and/or movement of the eye. Reliably and accurately detecting the pupil is important in several fields of medical diagnosis such as in rehabilitation, measurements of human intelligence, and diagnosis and treatment in the field of ophthalmology.
One known system for eye movement tracking comprises a grayscale camera that is used to capture an image of an eye, the captured image then being analyzed to detect the pupil position. The captured image of the eye contains objects, such as an eyelid, interfering with the image of the pupil. It is thus necessary to apply special measures to recover the image of the pupil and its center from the image of the eye.
Such a system and method is disclosed by S. I. Kim et al. in a paper entitled “A Fast Center of Pupil Detection Algorithm for VOG-Based Eye Movement Tracking” (Proc. of the IEEE 2005 Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, Sep. 1-4, 2005). The captured image is initially processed in order to obtain a “binary image”. Dark regions of the processed image from the camera (assumed to represent the pupil object) are distinguished from the bright regions (assumed to represent unnecessary data). In such a binary image a feature of interest such as the pupil object may be “segmented” which means that the pixels corresponding to the feature concerned are assigned one value in the binary representation, whereas other pixels not corresponding to the feature are assigned the other binary value. The system recognizes the center of the pupil by searching the largest circle from concentric circles that matches the segmented pupil object.
Unfortunately, a patient's long black eyelashes or false eyelashes can diminish the effectiveness of known pupil detection methods. Image portions representing eyelashes with mascara on the image usually overlap the pupil and are very often identified as part of the pupil. This phenomenon significantly influences the final result, i.e. calculated center is not a correct center of a pupil. That is also harmful to the pupil tracking algorithm, as pupil and eyelashes move with respect to each other.
Moreover, pupil detection based on searching for the largest circle from concentric circles cannot be applied successfully in camera systems that use prismatic split field rangefinders to determine the distance from eye to the camera. The captured image from the camera in such systems is divided into two fields. When the image is out of focus, the two fields of the image are displaced; when in focus, the fields are aligned to form a single image. Therefore, if pupil object is split, it is impossible to use the above solution to find matching circle and center of the pupil.